Abstract Heart failure affects over five million Americans and is a leading cause of death. A significant recent advancement in the treatment of heart failure has been cardiac resynchronization therapy (CRT) in which electrical pacing is used to improve mechanical pump function. However, about 30% of patients indicated for the procedure do not respond owing to a lack of predictive objective criteria for patient selection and lead placement. The overall goal of the proposed research is to address the challenges involved using multi-scale computational models of the heart for predicting patient outcomes of cardiac resynchronization therapy. There are three specific aims: (1) To develop three-dimensional multi-scale computational models of the failing human heart. While detailed models of ventricular electromechanics have already been developed and validated for animals, there is a need for comparable models of the failing human heart. Using published human data and clinically accessible measurements, we will develop and test a new model of ventricular electromechanics in the failing human heart; (2) To develop efficient methods for fitting parameters of three- dimensional multi-scale models of the heart to patient-specific clinical measurements. We will develop and test methods for adapting the baseline model of the failing human heart to individual heart failure patients making use of clinical measurements of ventricular chamber geometry, endocardial activity, hemodynamics, and pump function. We will test the hypothesis that adjusting a subset of model parameters will make it possible to match regional and global ventricular mechanical function and synchrony that we obtain in patients indicated for cardiac resynchronization therapy; (3) To validate patient-specific models of dyssynchronous heart failure for predicting responses to cardiac resynchronization therapy. Cardiac resynchronization augments cardiac function in moderate-to-severe heart failure with an intraventricular conduction delay, and can lead to reverse remodeling. By imposing ventricular pacing and computing global and local mechanical function with the patient-specific models, we propose to test the ability of multi-scale models of ventricular electromechanics to predict acute functional improvements with cardiac resynchronization therapy. We will test the hypothesis that the reduction in regional wall stress or an improvement in the synchrony of regional tension development computed by the model in response to resynchronization therapy predicts reverse remodeling after three months. We will also explore the use of patient-specific models to optimize the CRT pacing protocol. PUBLIC HEALTH RELEVANCE: Not only is cardiac disease the leading cause of death among citizens of the USA, but heart failure - when the heart fails to supply the body's demand of blood - affects all aspects of the lives of patients and curtails everyday activities. A significant recent advancement in the treatment of a common type of heart failure - in which the heart is activated slower than normal - has been cardiac resynchronization therapy. In this therapy, a pacemaker is used to improve the timing of the beating of the left and right ventricles. However, about 30% of patients do not respond to this therapy for unknown reasons. The specific focus of this proposal is the development of computer models of failing human hearts, which are patient-specific. The models can then be used to predict the outcome - and thus to improve the number of patients to respond - and optimize this pacemaker therapy for patients.